Baselines. Following [1]}, [2]}, the fine-tuning baseline is initialized with the last model parameters at each time and is naively trained for each frame; the re-training baseline is trained with the entire sequence of data following the signed distance function setup of SIREN [3]}. To further study the effect of the replay buffer, we also provide a re-initialization baseline that learns from scratch with the buffer data for each frame. Adam optimizer is adopted with a learning rate of \(0.0001\) . The data for each method are trained for 1500 epochs if not specified, while the first frame is trained for 10000 epochs to ensure nice initialization.
